Published September 4, 2020
| Version v1
Dataset
Open
Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations - data
Creators
- 1. Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University
- 2. University of Tübingen, Center for Integrative Neuroscience, Tübingen, Germany
- 3. University of Tübingen, Center for Integrative Neuroscience, Tübingen, Germany; Bernstein Center for Computational Neuroscience, Tübingen, Germany
Description
Data from the paper: Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.
Preprint: https://www.biorxiv.org/content/10.1101/840256v1
Marek A. Pedziwiatr
marek.pedziwi@gmail.com
September 2020
Files
data_pedziwiatr_meaning_maps.zip
Files
(2.1 GB)
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Additional details
Related works
- Is supplement to
- 10.5281/zenodo.3490592 (DOI)